How do you make data-driven decisions?

Few organizations don't have the ambition to work data-driven. Data is everywhere, and anyone who doesn't use that data to steer their business will sooner or later be left behind. That is, at least, the prevailing opinion in the business world, and of course we wholeheartedly endorse it. So we want to work data-driven. But how do you do that? And what does it actually mean to make data-driven decisions? In this blog, Data Strategy Leader Louis de Roo explains it to you.

What is data-driven decision-making not?

Let's start by naming what data-driven decision-making is not, because not every use of data contributes to data-driven decision-making:

• Data-driven decision-making is not: looking at a list of results and then deciding based on your experience or intuition.
• Data-driven decision-making is also not: conjuring up all available data on a screen and expecting everyone to understand what needs to happen.
• Data-driven decision-making is definitely not: making your decision on gut feeling and then digging up the data that supports your decision.

The examples above may seem obvious, but in daily practice they occur all too often: after all, what is the alternative? Blindly trusting your data and the underlying logic? Is the quality of the data good enough? Is the logic correct?

Which decision do you want to make?

The key lies in figuring out which decision you would actually like to make with your data. Years of output-driven business management have taught us to look at the outcomes, and then look within the existing process at where we can optimize without negatively affecting that output. But a data-driven decision works differently. With a data-driven decision, you try to make an optimal choice as early as possible in the process, after which your process delivers the desired result as smoothly as possible. Analyzing the end results can help to optimize the process further, but it comes too late to make fundamental decisions within the process. On top of that, on the output side of your process you are far more dependent on all kinds of exceptions that can occur throughout the process, and that will require special attention in any case – every process exception, after all, means that the regular steps could not be followed.

For example, measuring your delivery time to customers certainly holds valuable insights, but for improving your process that information comes too late. A good data-driven decision would be to increase or decrease your stock position based on your sales forecast, so that you always have enough in stock to deliver without delays. Looking at the realized delivery time then does allow you to check whether you have achieved your goal, and to flag any exceptions, but that evaluation needs to be fed back to the reorder point before you can make a truly data-driven decision here.

Mapping out your process

It is therefore essential to realize that some decisions don't get better by making them data-driven, and that it is important to map out your process properly before you pull all kinds of random data out of the closet to steer your process. As if that bit of self-reflection weren't enough, the next task awaiting you is to think carefully about the logic you will have to apply in your data-driven decision: at which threshold value do you have to take action? What are the signal points? Which measures do you react to, and which not?

Scalable decisions

Setting up a data-driven decision mechanism is complex, and takes time and insight. This is why, for many organizations, the reflex is to just do it the familiar way after all. That is, after all, less 'work'. For a single decision that is indeed true. Setting up decision logic to solve a one-off issue is therefore a waste of money and resources. That is why we look for those decisions that can contribute to better results in a scalable way.

Data-driven decision-making is therefore first and foremost about figuring out which decisions will actually get better through the use of data. And then letting those decisions improve your process. After that, there is nothing wrong with looking at data afterwards and being satisfied with your results.

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